Deep Learning in Real Time with TensorFlow, H2O.ai and Kafka Streams (Slides from JavaOne 2017)

Posted in Analytics, Apache Kafka, Big Data, Business Intelligence, Confluent, Deep Learning, Docker, Java / JEE, Kafka Streams, Machine Learning, Microservices, Open Source, Stream Processing on October 4th, 2017 by Kai Wähner

Early October… Like every year in October, it is time for JavaOne and Oracle Open World in San Francisco… I am glad to be back at this huge event again. My talk at JavaOne 2017 was all about deployment of analytic models to scalable production systems leveraging Apache Kafka and Kafka Streams. Let’s first look at the abstract. After that I attach the slides and refer to further material around this topic.

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Kafka Streams + H2O.ai + TensorFlow (Video Recording / Live Demo)

Posted in Analytics, Apache Kafka, Big Data, Kafka Streams, Machine Learning, Open Source, Stream Processing on September 7th, 2017 by Kai Wähner

I do a lot of presentations these days at meetups and conferences with one focus: How to leverage Apache Kafka and Kafka Streams to apply analytic models (built with H2O, TensorFlow, DeepLearning4J and other frameworks) to scalable, mission-critical environments. As many attendees have asked me, I created a video recording about this talk (focusing on live demos).

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Apache Kafka Streams + Machine Learning (Spark, TensorFlow, H2O.ai)

Posted in Analytics, Apache Kafka, Apache Spark, Big Data, Confluent, Hadoop, Integration, Kafka Connect, Kafka Streams, Machine Learning, Messaging, Microservices, Open Source, Stream Processing on May 23rd, 2017 by Kai Wähner

I started at Confluent in May 2017 to work as Technology Evangelist focusing on topics around the open source framework Apache Kafka. I think Machine Learning is one of the hottest buzzwords these days as it can add huge business value in any industry. Therefore, you will see various other posts from me around Apache Kafka (messaging), Kafka Connect (integration), Kafka Streams (stream processing), Confluent’s additional open source add-ons on top of Kafka (Schema Registry, Replicator, Auto Balancer, etc.). I will explain how to leverage all this for machine learning and other big data technologies in real world production scenarios.

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Why I Move (Back) to Open Source for Messaging, Integration and Stream Processing

Posted in Analytics, API Management, Big Data, Blockchain, Cloud, Cloud-Native, Docker, ESB, Hadoop, Internet of Things, Java / JEE, Machine Learning, Microservices, Middleware, SOA on May 1st, 2017 by Kai Wähner

After three great years at TIBCO Software, I move back to open source and join Confluent, a company focusing on the open source project Apache Kafka to build mission-critical, scalable infrastructures for messaging, integration and streaming analytics. Confluent is a Silicon Valley startup, still in the beginning of its journey, with a 700% growing business in 2016, and is exjustpected to grow significantly in 2017 again.

In this blog post, I want to share why I see the future for middleware and big data analytics in open source technologies, why I really like Confluent, what I will focus on in the next months, and why I am so excited about this next step in my career.

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Visual Analytics + Open Source Deep Learning Frameworks

Posted in Analytics, Big Data, Cloud, Hadoop, Machine Learning on April 24th, 2017 by Kai Wähner

Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.

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Agile Cloud-to-Cloud Integration with iPaaS, API Management and Blockchain

Posted in API Management, Blockchain, Cloud, Cloud-Native, Docker, EAI, ESB, Microservices, Middleware on April 23rd, 2017 by Kai Wähner

Cloud-to-Cloud integration is part of a hybrid integration architecture. It enables to implement quick and agile integration scenarios without the burden of setting up complex VM- or container-based infrastructures. One key use case for cloud-to-cloud integration is innovation using a fail-fast methodology where you realize new ideas quickly. You typically think in days or weeks, not in months. If an idea fails, you throw it away and start another new idea. If the idea works well, you scale it out and bring it into production to a on premise, cloud or hybrid infrastructure. Finally, you make expose the idea and make it easily available to any interested service consumer in your enterprise, partners or public end users.

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Cloud Native Middleware Microservices – 10 Lessons Learned (O’Reilly Software Architecture 2017, New York)

Posted in API Management, Cloud, Cloud-Native, Docker, EAI, ESB, Microservices, Middleware, SOA on April 5th, 2017 by Kai Wähner

I want to share my slide deck and video recordings from the talk “10 Lessons Learned from Building Cloud Native Middleware Microservices” at O’Reilly Software Architecture April 2017 in New York, USA in April 2017.

Abstract
Microservices are the next step after SOA: Services implement a limited set of functions; services are developed, deployed, and scaled independently; continuous delivery automates deployments. This way you get shorter time to results and increased flexibility. Containers improve things even more, offering a very lightweight and flexible deployment option.

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Case Study: From a Monolith to Cloud, Containers, Microservices

Posted in API Management, Cloud, Cloud-Native, Docker, EAI, ESB, Java / JEE, Microservices, Middleware, SOA on February 24th, 2017 by Kai Wähner

The following shows a case study about successfully moving from a very complex monolith system to a cloud-native architecture. The architecture leverages containers and Microservices. This solve issues such as high efforts for extending the system, and a very slow deployment process. The old system included a few huge Java applications and a complex integration middleware deployment.

The new architecture allows flexible development, deployment and operations of business and integration services. Besides, it is vendor-agnostic so that you can leverage on-premise hardware, different public cloud infrastructures, and cloud-native PaaS platforms.

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Blockchain, Integration, Streaming Analytics, Ethereum, Hyperledger

Posted in Analytics, Blockchain, ESB, Machine Learning, Middleware, SOA on February 24th, 2017 by Kai Wähner

In the fast few weeks, I have published a few articles, slide decks and videos around Blockchain, Middleware, Integration, Streaming Analytics, Ethereum, Hyperledger. I want to share the links here…

Blockchain – The Next Big Thing for Middleware

InfoQ article: “Blockchain – The Next Big Thing for Middleware”

Key takeaways:

  • Blockchain is not just for Bitcoin
  • A blockchain is a protocol and ledger for building an immutable historical record of transactions
  • There is no new technology behind blockchain, just established components combined in a new way
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Comparison: Data Preparation vs. Inline Data Wrangling in Machine Learning and Deep Learning Projects

Posted in Analytics, Big Data, Business Intelligence, Hadoop on February 13th, 2017 by Kai Wähner

I want to highlight a new presentation about Data Preparation in Data Science projects:

“Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling  in Machine Learning / Deep Learning Projects”

Data Preparation as Key for Success in Data Science Projects

A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.

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